In the realm of multi-agent reinforcement learning (MARL), achieving high performance is crucial for a successful multi-agent system.

Energy-efficient computing is of primary importance to the effective deployment of deep neural networks (DNNs), particularly in edge devices and in on-chip AI systems. Increasing DNN computation's energy efficiency and lowering its carbon footprint require iterative efforts from both chip designers and algorithm developers.

For the egocentric HOI, in addition to perceiving semantics e.g., "what" interaction is occurring, capturing "where" the interaction specifically manifests in 3D space is

Neural Information Processing Systems http://nips.cc/

We propose ChatCam, a system that allows users to control camera operations through natural language interaction.

The23 proposed SSCM does coverthe case of non-zero variance, but currently the identifiability proof is only shown in a24 specific case. Inour simulations under non-zero variance settings, we neverobserved that the procedure converged25 to wrong solutions, suggesting that the non-zero-variance case is also identifiable. For the fMRI and cellular data, the null hypothesis was rejected at significance level 0.01. Regarding causal28 structure variation, for fMRI data, it is well-known that neural connectivities may change across different external29 stimuliorintrinsicstates. Forcellular32 data, causal structure may be different across conditions/interventions.(0)Theyare different.

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